Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations101693
Missing cells316888
Missing cells (%)11.5%
Duplicate rows1097
Duplicate rows (%)1.1%
Total size in memory20.9 MiB
Average record size in memory216.0 B

Variable types

Categorical5
Text2
Numeric20

Alerts

Crawled_date has constant value "2024.12.5" Constant
Dataset has 1097 (1.1%) duplicate rowsDuplicates
Average_Rating is highly overall correlated with Hotel_Name and 6 other fieldsHigh correlation
FOG Index is highly overall correlated with Flesch Reading EaseHigh correlation
Flesch Reading Ease is highly overall correlated with FOG IndexHigh correlation
Hotel_Name is highly overall correlated with Average_Rating and 8 other fieldsHigh correlation
Num_of_Ratings is highly overall correlated with Hotel_Name and 1 other fieldsHigh correlation
Rating is highly overall correlated with valenceHigh correlation
breadth is highly overall correlated with depth and 1 other fieldsHigh correlation
cleanliness_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
comfort_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
depth is highly overall correlated with breadthHigh correlation
employee_friendliness_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
facility_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
hotel_grade is highly overall correlated with Average_Rating and 4 other fieldsHigh correlation
location_score is highly overall correlated with Hotel_NameHigh correlation
sentiment_score_discrete is highly overall correlated with valenceHigh correlation
text_length is highly overall correlated with breadthHigh correlation
valence is highly overall correlated with Rating and 1 other fieldsHigh correlation
value_for_money_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
is_photo is highly imbalanced (71.3%) Imbalance
Hotel_Name has 12188 (12.0%) missing values Missing
Review_Text has 12188 (12.0%) missing values Missing
Rating has 12188 (12.0%) missing values Missing
Average_Rating has 12188 (12.0%) missing values Missing
Num_of_Ratings has 12188 (12.0%) missing values Missing
Helpfulness has 12188 (12.0%) missing values Missing
is_photo has 12188 (12.0%) missing values Missing
review_title has 12188 (12.0%) missing values Missing
hotel_grade has 12188 (12.0%) missing values Missing
employee_friendliness_score has 12188 (12.0%) missing values Missing
facility_score has 12188 (12.0%) missing values Missing
cleanliness_score has 12188 (12.0%) missing values Missing
comfort_score has 12188 (12.0%) missing values Missing
value_for_money_score has 12188 (12.0%) missing values Missing
location_score has 12188 (12.0%) missing values Missing
Crawled_date has 12188 (12.0%) missing values Missing
title_length has 12188 (12.0%) missing values Missing
text_length has 12188 (12.0%) missing values Missing
Deviation of star ratings has 12188 (12.0%) missing values Missing
FOG Index has 12188 (12.0%) missing values Missing
Flesch Reading Ease has 12188 (12.0%) missing values Missing
depth has 12188 (12.0%) missing values Missing
breadth has 12188 (12.0%) missing values Missing
valence has 12188 (12.0%) missing values Missing
sentiment_score_discrete has 12188 (12.0%) missing values Missing
arousal has 12188 (12.0%) missing values Missing
Helpfulness has 81272 (79.9%) zeros Zeros
Deviation of star ratings has 2451 (2.4%) zeros Zeros

Reproduction

Analysis started2025-02-06 04:41:25.434328
Analysis finished2025-02-06 04:41:48.099655
Duration22.67 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Hotel_Name
Categorical

High correlation  Missing 

Distinct33
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Memory size794.6 KiB
thistletower
 
4646
lancaster-gate
 
4637
zedwell-trocaderor
 
4632
stgileshotel
 
4621
z-trafalgar
 
4392
Other values (28)
66577 

Length

Max length35
Median length22
Mean length15.818826
Min length3

Characters and Unicode

Total characters1415864
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstudios2let
2nd rowstudios2let
3rd rowstudios2let
4th rowstudios2let
5th rowstudios2let

Common Values

ValueCountFrequency (%)
thistletower 4646
 
4.6%
lancaster-gate 4637
 
4.6%
zedwell-trocaderor 4632
 
4.6%
stgileshotel 4621
 
4.5%
z-trafalgar 4392
 
4.3%
nyx-hotel-london-by-leonardo-hotels 3567
 
3.5%
radissonblugrafton 3472
 
3.4%
sidneyhotel 3316
 
3.3%
studios2let 3294
 
3.2%
montcalm-chilworth-townhouse 3134
 
3.1%
Other values (23) 49794
49.0%
(Missing) 12188
 
12.0%

Length

2025-02-06T13:41:48.129036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
thistletower 4646
 
5.2%
lancaster-gate 4637
 
5.2%
zedwell-trocaderor 4632
 
5.2%
stgileshotel 4621
 
5.2%
z-trafalgar 4392
 
4.9%
nyx-hotel-london-by-leonardo-hotels 3567
 
4.0%
radissonblugrafton 3472
 
3.9%
sidneyhotel 3316
 
3.7%
studios2let 3294
 
3.7%
montcalm-chilworth-townhouse 3134
 
3.5%
Other values (23) 49794
55.6%

Most occurring characters

ValueCountFrequency (%)
e 152385
10.8%
t 144243
10.2%
o 143989
10.2%
l 125444
 
8.9%
a 104620
 
7.4%
r 99022
 
7.0%
n 85007
 
6.0%
s 79298
 
5.6%
- 71310
 
5.0%
h 63778
 
4.5%
Other values (16) 346768
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1415864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 152385
10.8%
t 144243
10.2%
o 143989
10.2%
l 125444
 
8.9%
a 104620
 
7.4%
r 99022
 
7.0%
n 85007
 
6.0%
s 79298
 
5.6%
- 71310
 
5.0%
h 63778
 
4.5%
Other values (16) 346768
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1415864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 152385
10.8%
t 144243
10.2%
o 143989
10.2%
l 125444
 
8.9%
a 104620
 
7.4%
r 99022
 
7.0%
n 85007
 
6.0%
s 79298
 
5.6%
- 71310
 
5.0%
h 63778
 
4.5%
Other values (16) 346768
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1415864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 152385
10.8%
t 144243
10.2%
o 143989
10.2%
l 125444
 
8.9%
a 104620
 
7.4%
r 99022
 
7.0%
n 85007
 
6.0%
s 79298
 
5.6%
- 71310
 
5.0%
h 63778
 
4.5%
Other values (16) 346768
24.5%

Review_Text
Text

Missing 

Distinct89400
Distinct (%)99.9%
Missing12188
Missing (%)12.0%
Memory size794.6 KiB
2025-02-06T13:41:48.350127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3585
Median length1846
Mean length206.28943
Min length1

Characters and Unicode

Total characters18463935
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89390 ?
Unique (%)99.9%

Sample

1st rowPerfect location with good connections and shops and pubs
2nd rowThe room had everything you needed. Near to amenities, was good room for price just needs little updatingThe bed was so hard it felt like sleeping on a hard floor, you had to make sure you had something on your feet as flooring pinched you feet needs changing
3rd rowConveniently nearby St. Pancras, very small but clean and pleasant room (first floor with small balcony to street side). Interesting area.Luggage service can be improved by offering to lock luggage up instead of it just being put into the hall with all risks on the guests.
4th rowReception staffed 24 hours a day.All good.
5th rowVery convenient to Kings Cross and the cityA little dated could do with a lick of paint
ValueCountFrequency (%)
the 179806
 
5.5%
and 125523
 
3.9%
was 105549
 
3.3%
to 80893
 
2.5%
a 76820
 
2.4%
room 60728
 
1.9%
in 52097
 
1.6%
very 46505
 
1.4%
for 42663
 
1.3%
location 41964
 
1.3%
Other values (73889) 2433833
75.0%
2025-02-06T13:41:48.638871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3161558
17.1%
e 1736815
 
9.4%
o 1346144
 
7.3%
t 1302435
 
7.1%
a 1262850
 
6.8%
n 971249
 
5.3%
r 884211
 
4.8%
i 870945
 
4.7%
s 818224
 
4.4%
l 704584
 
3.8%
Other values (66) 5404920
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18463935
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3161558
17.1%
e 1736815
 
9.4%
o 1346144
 
7.3%
t 1302435
 
7.1%
a 1262850
 
6.8%
n 971249
 
5.3%
r 884211
 
4.8%
i 870945
 
4.7%
s 818224
 
4.4%
l 704584
 
3.8%
Other values (66) 5404920
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18463935
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3161558
17.1%
e 1736815
 
9.4%
o 1346144
 
7.3%
t 1302435
 
7.1%
a 1262850
 
6.8%
n 971249
 
5.3%
r 884211
 
4.8%
i 870945
 
4.7%
s 818224
 
4.4%
l 704584
 
3.8%
Other values (66) 5404920
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18463935
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3161558
17.1%
e 1736815
 
9.4%
o 1346144
 
7.3%
t 1302435
 
7.1%
a 1262850
 
6.8%
n 971249
 
5.3%
r 884211
 
4.8%
i 870945
 
4.7%
s 818224
 
4.4%
l 704584
 
3.8%
Other values (66) 5404920
29.3%

Rating
Real number (ℝ)

High correlation  Missing 

Distinct24
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean7.714235
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:48.689662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9163799
Coefficient of variation (CV)0.24842125
Kurtosis1.9857144
Mean7.714235
Median Absolute Deviation (MAD)1
Skewness-1.2857857
Sum690462.6
Variance3.672512
MonotonicityNot monotonic
2025-02-06T13:41:48.720685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8 26129
25.7%
9 16479
16.2%
10 15234
15.0%
7 14990
14.7%
6 6451
 
6.3%
5 3941
 
3.9%
4 2183
 
2.1%
3 1656
 
1.6%
1 1573
 
1.5%
2 799
 
0.8%
Other values (14) 70
 
0.1%
(Missing) 12188
12.0%
ValueCountFrequency (%)
1 1573
 
1.5%
2 799
 
0.8%
2.5 1
 
< 0.1%
2.9 1
 
< 0.1%
3 1656
1.6%
4 2183
2.1%
4.6 1
 
< 0.1%
5 3941
3.9%
5.4 2
 
< 0.1%
5.8 1
 
< 0.1%
ValueCountFrequency (%)
10 15234
15.0%
9.6 15
 
< 0.1%
9.2 12
 
< 0.1%
9 16479
16.2%
8.8 7
 
< 0.1%
8.3 7
 
< 0.1%
8 26129
25.7%
7.9 7
 
< 0.1%
7.5 5
 
< 0.1%
7.1 3
 
< 0.1%

Average_Rating
Real number (ℝ)

High correlation  Missing 

Distinct15
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean7.8322563
Minimum7
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:48.748218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q17.6
median7.7
Q38.1
95-th percentile8.6
Maximum8.7
Range1.7
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.42988557
Coefficient of variation (CV)0.054886556
Kurtosis-0.37402998
Mean7.8322563
Median Absolute Deviation (MAD)0.2
Skewness0.068699345
Sum701026.1
Variance0.1848016
MonotonicityNot monotonic
2025-02-06T13:41:48.778354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7.7 21807
21.4%
7.8 8749
8.6%
8.4 7959
 
7.8%
7.9 7510
 
7.4%
7.4 7313
 
7.2%
7 6497
 
6.4%
8.3 5300
 
5.2%
7.6 5222
 
5.1%
8.6 4480
 
4.4%
8 4100
 
4.0%
Other values (5) 10568
10.4%
(Missing) 12188
12.0%
ValueCountFrequency (%)
7 6497
 
6.4%
7.1 1907
 
1.9%
7.4 7313
 
7.2%
7.5 2065
 
2.0%
7.6 5222
 
5.1%
7.7 21807
21.4%
7.8 8749
8.6%
7.9 7510
 
7.4%
8 4100
 
4.0%
8.1 2409
 
2.4%
ValueCountFrequency (%)
8.7 2423
 
2.4%
8.6 4480
 
4.4%
8.4 7959
 
7.8%
8.3 5300
 
5.2%
8.2 1764
 
1.7%
8.1 2409
 
2.4%
8 4100
 
4.0%
7.9 7510
 
7.4%
7.8 8749
8.6%
7.7 21807
21.4%

Num_of_Ratings
Real number (ℝ)

High correlation  Missing 

Distinct33
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean11821.248
Minimum5613
Maximum39497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:48.810280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5613
5-th percentile5898
Q16664
median9394
Q313923
95-th percentile39497
Maximum39497
Range33884
Interquartile range (IQR)7259

Descriptive statistics

Standard deviation7556.399
Coefficient of variation (CV)0.63922176
Kurtosis6.5265364
Mean11821.248
Median Absolute Deviation (MAD)3059
Skewness2.5057259
Sum1.0580608 × 109
Variance57099167
MonotonicityNot monotonic
2025-02-06T13:41:48.845793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
20956 4646
 
4.6%
14445 4637
 
4.6%
39497 4632
 
4.6%
14989 4621
 
4.5%
13923 4392
 
4.3%
9394 3567
 
3.5%
9315 3472
 
3.4%
12641 3316
 
3.3%
11670 3294
 
3.2%
9205 3134
 
3.1%
Other values (23) 49794
49.0%
(Missing) 12188
 
12.0%
ValueCountFrequency (%)
5613 1780
1.8%
5715 1836
1.8%
5898 1764
1.7%
5932 2135
2.1%
5933 2256
2.2%
6120 1928
1.9%
6248 1619
1.6%
6277 2297
2.3%
6335 2002
2.0%
6404 1876
1.8%
ValueCountFrequency (%)
39497 4632
4.6%
20956 4646
4.6%
15320 2681
2.6%
14989 4621
4.5%
14445 4637
4.6%
13923 4392
4.3%
12641 3316
3.3%
12340 2795
2.7%
11670 3294
3.2%
11045 2644
2.6%

Helpfulness
Real number (ℝ)

Missing  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean0.10799397
Minimum0
Maximum14
Zeros81272
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:48.874182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37722244
Coefficient of variation (CV)3.4929955
Kurtosis60.928616
Mean0.10799397
Median Absolute Deviation (MAD)0
Skewness5.3359025
Sum9666
Variance0.14229677
MonotonicityNot monotonic
2025-02-06T13:41:48.900872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 81272
79.9%
1 7145
 
7.0%
2 870
 
0.9%
3 152
 
0.1%
4 36
 
< 0.1%
5 18
 
< 0.1%
6 7
 
< 0.1%
10 2
 
< 0.1%
7 1
 
< 0.1%
14 1
 
< 0.1%
(Missing) 12188
 
12.0%
ValueCountFrequency (%)
0 81272
79.9%
1 7145
 
7.0%
2 870
 
0.9%
3 152
 
0.1%
4 36
 
< 0.1%
5 18
 
< 0.1%
6 7
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 2
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 7
 
< 0.1%
5 18
 
< 0.1%
4 36
 
< 0.1%
3 152
 
0.1%
2 870
 
0.9%
1 7145
7.0%

is_photo
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Memory size794.6 KiB
0.0
85009 
1.0
 
4496

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters268515
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 85009
83.6%
1.0 4496
 
4.4%
(Missing) 12188
 
12.0%

Length

2025-02-06T13:41:48.930714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T13:41:48.953337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 85009
95.0%
1.0 4496
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 174514
65.0%
. 89505
33.3%
1 4496
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 268515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 174514
65.0%
. 89505
33.3%
1 4496
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 268515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 174514
65.0%
. 89505
33.3%
1 4496
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 268515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 174514
65.0%
. 89505
33.3%
1 4496
 
1.7%

review_title
Text

Missing 

Distinct49587
Distinct (%)55.4%
Missing12188
Missing (%)12.0%
Memory size794.6 KiB
2025-02-06T13:41:49.165594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length120
Median length105
Mean length29.704452
Min length1

Characters and Unicode

Total characters2658697
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47408 ?
Unique (%)53.0%

Sample

1st rowExceptional
2nd rowVery good
3rd rowConvenient location
4th rowPeaceful position in an elegant street close to 3 major stations and the Bloomsbury area.
5th rowGreat little gem in the city centre
ValueCountFrequency (%)
good 24888
 
5.4%
location 16216
 
3.5%
and 15769
 
3.4%
very 15704
 
3.4%
great 14151
 
3.1%
stay 13439
 
2.9%
a 12645
 
2.8%
for 11823
 
2.6%
the 11114
 
2.4%
hotel 10705
 
2.3%
Other values (10152) 310781
68.0%
2025-02-06T13:41:49.506249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
369116
13.9%
e 248608
 
9.4%
o 234573
 
8.8%
a 197436
 
7.4%
t 191721
 
7.2%
n 149773
 
5.6%
l 128794
 
4.8%
r 127999
 
4.8%
i 122653
 
4.6%
s 92039
 
3.5%
Other values (64) 795985
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2658697
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
369116
13.9%
e 248608
 
9.4%
o 234573
 
8.8%
a 197436
 
7.4%
t 191721
 
7.2%
n 149773
 
5.6%
l 128794
 
4.8%
r 127999
 
4.8%
i 122653
 
4.6%
s 92039
 
3.5%
Other values (64) 795985
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2658697
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
369116
13.9%
e 248608
 
9.4%
o 234573
 
8.8%
a 197436
 
7.4%
t 191721
 
7.2%
n 149773
 
5.6%
l 128794
 
4.8%
r 127999
 
4.8%
i 122653
 
4.6%
s 92039
 
3.5%
Other values (64) 795985
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2658697
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
369116
13.9%
e 248608
 
9.4%
o 234573
 
8.8%
a 197436
 
7.4%
t 191721
 
7.2%
n 149773
 
5.6%
l 128794
 
4.8%
r 127999
 
4.8%
i 122653
 
4.6%
s 92039
 
3.5%
Other values (64) 795985
29.9%

hotel_grade
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Memory size794.6 KiB
4.0
39990 
3.0
36148 
5.0
6701 
0.0
4632 
2.0
 
2034

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters268515
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0 39990
39.3%
3.0 36148
35.5%
5.0 6701
 
6.6%
0.0 4632
 
4.6%
2.0 2034
 
2.0%
(Missing) 12188
 
12.0%

Length

2025-02-06T13:41:49.555952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T13:41:49.577602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4.0 39990
44.7%
3.0 36148
40.4%
5.0 6701
 
7.5%
0.0 4632
 
5.2%
2.0 2034
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 94137
35.1%
. 89505
33.3%
4 39990
14.9%
3 36148
 
13.5%
5 6701
 
2.5%
2 2034
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 268515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 94137
35.1%
. 89505
33.3%
4 39990
14.9%
3 36148
 
13.5%
5 6701
 
2.5%
2 2034
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 268515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 94137
35.1%
. 89505
33.3%
4 39990
14.9%
3 36148
 
13.5%
5 6701
 
2.5%
2 2034
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 268515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 94137
35.1%
. 89505
33.3%
4 39990
14.9%
3 36148
 
13.5%
5 6701
 
2.5%
2 2034
 
0.8%

employee_friendliness_score
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean8.5347634
Minimum7.5
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:49.602528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.5
5-th percentile8
Q18.3
median8.6
Q38.7
95-th percentile9.1
Maximum9.1
Range1.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.36889506
Coefficient of variation (CV)0.043222647
Kurtosis0.69953941
Mean8.5347634
Median Absolute Deviation (MAD)0.2
Skewness-0.7107023
Sum763904
Variance0.13608357
MonotonicityNot monotonic
2025-02-06T13:41:49.629900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8.7 18186
17.9%
8.6 13683
13.5%
8.1 9253
9.1%
8.4 8871
8.7%
9.1 8651
8.5%
8.5 6722
 
6.6%
9 6027
 
5.9%
8.3 5359
 
5.3%
8.8 4287
 
4.2%
7.5 3783
 
3.7%
Other values (2) 4683
 
4.6%
(Missing) 12188
12.0%
ValueCountFrequency (%)
7.5 3783
 
3.7%
8 2681
 
2.6%
8.1 9253
9.1%
8.2 2002
 
2.0%
8.3 5359
 
5.3%
8.4 8871
8.7%
8.5 6722
 
6.6%
8.6 13683
13.5%
8.7 18186
17.9%
8.8 4287
 
4.2%
ValueCountFrequency (%)
9.1 8651
8.5%
9 6027
 
5.9%
8.8 4287
 
4.2%
8.7 18186
17.9%
8.6 13683
13.5%
8.5 6722
 
6.6%
8.4 8871
8.7%
8.3 5359
 
5.3%
8.2 2002
 
2.0%
8.1 9253
9.1%

facility_score
Real number (ℝ)

High correlation  Missing 

Distinct17
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean7.8393822
Minimum6.9
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:49.658431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile6.9
Q17.5
median7.8
Q38.3
95-th percentile8.7
Maximum8.7
Range1.8
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.50174873
Coefficient of variation (CV)0.064003606
Kurtosis-0.73232438
Mean7.8393822
Median Absolute Deviation (MAD)0.3
Skewness0.074828239
Sum701663.9
Variance0.25175179
MonotonicityNot monotonic
2025-02-06T13:41:49.689036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.8 12069
11.9%
7.5 11924
11.7%
7.6 8815
8.7%
8.7 8634
8.5%
6.9 6497
 
6.4%
8.3 5696
 
5.6%
8 5570
 
5.5%
7.2 4632
 
4.6%
8.4 4392
 
4.3%
7.7 3916
 
3.9%
Other values (7) 17360
17.1%
(Missing) 12188
12.0%
ValueCountFrequency (%)
6.9 6497
6.4%
7.2 4632
 
4.6%
7.3 1907
 
1.9%
7.4 2681
 
2.6%
7.5 11924
11.7%
7.6 8815
8.7%
7.7 3916
 
3.9%
7.8 12069
11.9%
7.9 2256
 
2.2%
8 5570
5.5%
ValueCountFrequency (%)
8.7 8634
8.5%
8.6 1836
 
1.8%
8.5 2505
 
2.5%
8.4 4392
 
4.3%
8.3 5696
5.6%
8.2 2409
 
2.4%
8.1 3766
 
3.7%
8 5570
5.5%
7.9 2256
 
2.2%
7.8 12069
11.9%

cleanliness_score
Real number (ℝ)

High correlation  Missing 

Distinct14
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean8.2468521
Minimum7.3
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:49.718944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.3
Q18
median8.2
Q38.7
95-th percentile8.8
Maximum9.1
Range1.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.44250269
Coefficient of variation (CV)0.053657163
Kurtosis-0.33591991
Mean8.2468521
Median Absolute Deviation (MAD)0.3
Skewness-0.17813145
Sum738134.5
Variance0.19580863
MonotonicityNot monotonic
2025-02-06T13:41:49.749433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
8.7 11852
11.7%
7.9 9854
9.7%
8.2 9297
9.1%
8.1 9000
8.9%
8.8 8716
8.6%
8 8484
8.3%
8.3 7670
7.5%
8.4 7286
7.2%
7.3 4621
 
4.5%
9.1 4259
 
4.2%
Other values (4) 8466
8.3%
(Missing) 12188
12.0%
ValueCountFrequency (%)
7.3 4621
4.5%
7.4 1876
 
1.8%
7.5 1907
 
1.9%
7.8 2681
 
2.6%
7.9 9854
9.7%
8 8484
8.3%
8.1 9000
8.9%
8.2 9297
9.1%
8.3 7670
7.5%
8.4 7286
7.2%
ValueCountFrequency (%)
9.1 4259
 
4.2%
8.8 8716
8.6%
8.7 11852
11.7%
8.5 2002
 
2.0%
8.4 7286
7.2%
8.3 7670
7.5%
8.2 9297
9.1%
8.1 9000
8.9%
8 8484
8.3%
7.9 9854
9.7%

comfort_score
Real number (ℝ)

High correlation  Missing 

Distinct16
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean8.2400223
Minimum7.3
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:49.781498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.3
Q18
median8.2
Q38.7
95-th percentile8.9
Maximum9.1
Range1.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.46634394
Coefficient of variation (CV)0.056594985
Kurtosis-0.52072817
Mean8.2400223
Median Absolute Deviation (MAD)0.3
Skewness-0.13031166
Sum737523.2
Variance0.21747667
MonotonicityNot monotonic
2025-02-06T13:41:49.814325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8 12779
12.6%
7.9 9378
9.2%
8.2 8674
8.5%
8.1 8445
8.3%
8.8 8201
8.1%
7.3 6497
6.4%
8.3 6450
6.3%
8.9 6211
6.1%
8.5 5236
 
5.1%
8.7 4392
 
4.3%
Other values (6) 13242
13.0%
(Missing) 12188
12.0%
ValueCountFrequency (%)
7.3 6497
6.4%
7.4 1907
 
1.9%
7.8 3294
 
3.2%
7.9 9378
9.2%
8 12779
12.6%
8.1 8445
8.3%
8.2 8674
8.5%
8.3 6450
6.3%
8.4 1780
 
1.8%
8.5 5236
5.1%
ValueCountFrequency (%)
9.1 2423
 
2.4%
9 1836
 
1.8%
8.9 6211
6.1%
8.8 8201
8.1%
8.7 4392
4.3%
8.6 2002
 
2.0%
8.5 5236
5.1%
8.4 1780
 
1.8%
8.3 6450
6.3%
8.2 8674
8.5%

value_for_money_score
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean7.7094911
Minimum7
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:49.844301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q17.4
median7.7
Q37.9
95-th percentile8.2
Maximum8.3
Range1.3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.32641101
Coefficient of variation (CV)0.042338853
Kurtosis-0.65862063
Mean7.7094911
Median Absolute Deviation (MAD)0.2
Skewness-0.16088603
Sum690038
Variance0.10654415
MonotonicityNot monotonic
2025-02-06T13:41:49.873187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.9 20241
19.9%
7.4 13180
13.0%
7.5 10425
10.3%
7.7 8546
8.4%
8.1 7952
 
7.8%
7.6 7760
 
7.6%
7.3 4746
 
4.7%
7 4621
 
4.5%
8.2 4392
 
4.3%
8 4042
 
4.0%
(Missing) 12188
12.0%
ValueCountFrequency (%)
7 4621
 
4.5%
7.3 4746
 
4.7%
7.4 13180
13.0%
7.5 10425
10.3%
7.6 7760
 
7.6%
7.7 8546
8.4%
7.9 20241
19.9%
8 4042
 
4.0%
8.1 7952
 
7.8%
8.2 4392
 
4.3%
ValueCountFrequency (%)
8.3 3600
 
3.5%
8.2 4392
 
4.3%
8.1 7952
 
7.8%
8 4042
 
4.0%
7.9 20241
19.9%
7.7 8546
8.4%
7.6 7760
 
7.6%
7.5 10425
10.3%
7.4 13180
13.0%
7.3 4746
 
4.7%

location_score
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean9.1767086
Minimum8.2
Maximum9.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:49.898303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile8.6
Q19
median9.1
Q39.4
95-th percentile9.6
Maximum9.7
Range1.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.31118887
Coefficient of variation (CV)0.033910728
Kurtosis0.54790958
Mean9.1767086
Median Absolute Deviation (MAD)0.2
Skewness-0.53207634
Sum821361.3
Variance0.096838514
MonotonicityNot monotonic
2025-02-06T13:41:49.926863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9.1 14059
13.8%
8.9 13828
13.6%
9 11122
10.9%
9.4 10530
10.4%
9.3 9270
9.1%
9.5 7290
7.2%
9.6 7055
6.9%
9.2 5657
5.6%
8.6 4395
 
4.3%
9.7 4392
 
4.3%
(Missing) 12188
12.0%
ValueCountFrequency (%)
8.2 1907
 
1.9%
8.6 4395
 
4.3%
8.9 13828
13.6%
9 11122
10.9%
9.1 14059
13.8%
9.2 5657
5.6%
9.3 9270
9.1%
9.4 10530
10.4%
9.5 7290
7.2%
9.6 7055
6.9%
ValueCountFrequency (%)
9.7 4392
 
4.3%
9.6 7055
6.9%
9.5 7290
7.2%
9.4 10530
10.4%
9.3 9270
9.1%
9.2 5657
5.6%
9.1 14059
13.8%
9 11122
10.9%
8.9 13828
13.6%
8.6 4395
 
4.3%

Crawled_date
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Memory size794.6 KiB
2024.12.5
89505 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters805545
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024.12.5
2nd row2024.12.5
3rd row2024.12.5
4th row2024.12.5
5th row2024.12.5

Common Values

ValueCountFrequency (%)
2024.12.5 89505
88.0%
(Missing) 12188
 
12.0%

Length

2025-02-06T13:41:49.957580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T13:41:49.974679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2024.12.5 89505
100.0%

Most occurring characters

ValueCountFrequency (%)
2 268515
33.3%
. 179010
22.2%
0 89505
 
11.1%
4 89505
 
11.1%
1 89505
 
11.1%
5 89505
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 805545
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 268515
33.3%
. 179010
22.2%
0 89505
 
11.1%
4 89505
 
11.1%
1 89505
 
11.1%
5 89505
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 805545
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 268515
33.3%
. 179010
22.2%
0 89505
 
11.1%
4 89505
 
11.1%
1 89505
 
11.1%
5 89505
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 805545
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 268515
33.3%
. 179010
22.2%
0 89505
 
11.1%
4 89505
 
11.1%
1 89505
 
11.1%
5 89505
 
11.1%

title_length
Real number (ℝ)

Missing 

Distinct29
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean5.1086308
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:49.997022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile15
Maximum29
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.8223059
Coefficient of variation (CV)0.94395271
Kurtosis2.0201067
Mean5.1086308
Median Absolute Deviation (MAD)2
Skewness1.4957671
Sum457248
Variance23.254634
MonotonicityNot monotonic
2025-02-06T13:41:50.030985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 24998
24.6%
2 15310
15.1%
4 6308
 
6.2%
5 6208
 
6.1%
6 5843
 
5.7%
7 4767
 
4.7%
3 4654
 
4.6%
8 3866
 
3.8%
9 3116
 
3.1%
10 2491
 
2.4%
Other values (19) 11944
11.7%
(Missing) 12188
12.0%
ValueCountFrequency (%)
1 24998
24.6%
2 15310
15.1%
3 4654
 
4.6%
4 6308
 
6.2%
5 6208
 
6.1%
6 5843
 
5.7%
7 4767
 
4.7%
8 3866
 
3.8%
9 3116
 
3.1%
10 2491
 
2.4%
ValueCountFrequency (%)
29 2
 
< 0.1%
28 6
 
< 0.1%
27 19
 
< 0.1%
26 33
 
< 0.1%
25 86
 
0.1%
24 171
0.2%
23 239
0.2%
22 284
0.3%
21 340
0.3%
20 419
0.4%

text_length
Real number (ℝ)

High correlation  Missing 

Distinct408
Distinct (%)0.5%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean36.369298
Minimum1
Maximum654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:50.067800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median23
Q347
95-th percentile111
Maximum654
Range653
Interquartile range (IQR)36

Descriptive statistics

Standard deviation40.564263
Coefficient of variation (CV)1.1153436
Kurtosis16.986613
Mean36.369298
Median Absolute Deviation (MAD)15
Skewness3.1966573
Sum3255234
Variance1645.4594
MonotonicityNot monotonic
2025-02-06T13:41:50.110080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2601
 
2.6%
5 2562
 
2.5%
9 2531
 
2.5%
7 2530
 
2.5%
8 2495
 
2.5%
10 2375
 
2.3%
4 2341
 
2.3%
11 2254
 
2.2%
12 2248
 
2.2%
14 2061
 
2.0%
Other values (398) 65507
64.4%
(Missing) 12188
 
12.0%
ValueCountFrequency (%)
1 603
 
0.6%
2 1263
1.2%
3 1844
1.8%
4 2341
2.3%
5 2562
2.5%
6 2601
2.6%
7 2530
2.5%
8 2495
2.5%
9 2531
2.5%
10 2375
2.3%
ValueCountFrequency (%)
654 1
< 0.1%
568 1
< 0.1%
527 1
< 0.1%
510 1
< 0.1%
503 1
< 0.1%
493 1
< 0.1%
491 1
< 0.1%
470 1
< 0.1%
469 1
< 0.1%
468 1
< 0.1%

time_lapsed
Real number (ℝ)

Distinct1101
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean543.49699
Minimum0
Maximum1100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:50.151722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61
Q1276
median535
Q3822
95-th percentile1022
Maximum1100
Range1100
Interquartile range (IQR)546

Descriptive statistics

Standard deviation310.46021
Coefficient of variation (CV)0.5712271
Kurtosis-1.1744317
Mean543.49699
Median Absolute Deviation (MAD)274
Skewness0.012226574
Sum55269839
Variance96385.539
MonotonicityNot monotonic
2025-02-06T13:41:50.248431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521 208
 
0.2%
514 204
 
0.2%
1011 190
 
0.2%
527 184
 
0.2%
885 184
 
0.2%
283 184
 
0.2%
1018 174
 
0.2%
913 174
 
0.2%
1004 172
 
0.2%
640 171
 
0.2%
Other values (1091) 99848
98.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 4
 
< 0.1%
2 36
 
< 0.1%
3 80
0.1%
4 76
0.1%
5 103
0.1%
6 108
0.1%
7 94
0.1%
8 66
0.1%
9 97
0.1%
ValueCountFrequency (%)
1100 3
 
< 0.1%
1099 40
 
< 0.1%
1098 52
 
0.1%
1097 58
0.1%
1096 75
0.1%
1095 143
0.1%
1094 86
0.1%
1093 75
0.1%
1092 77
0.1%
1091 68
0.1%

Deviation of star ratings
Real number (ℝ)

Missing  Zeros 

Distinct77
Distinct (%)0.1%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean1.3441741
Minimum0
Maximum7.7
Zeros2451
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:50.290943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.4
median1
Q31.8
95-th percentile4
Maximum7.7
Range7.7
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.2904159
Coefficient of variation (CV)0.96000655
Kurtosis5.0862224
Mean1.3441741
Median Absolute Deviation (MAD)0.6
Skewness2.0499369
Sum120310.3
Variance1.6651732
MonotonicityNot monotonic
2025-02-06T13:41:50.335052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 8846
 
8.7%
0.6 7299
 
7.2%
0.4 7184
 
7.1%
1.3 5925
 
5.8%
0.7 5920
 
5.8%
1.4 4800
 
4.7%
1 3906
 
3.8%
0.2 3124
 
3.1%
1.6 3036
 
3.0%
0.1 2979
 
2.9%
Other values (67) 36486
35.9%
(Missing) 12188
 
12.0%
ValueCountFrequency (%)
0 2451
 
2.4%
0.1 2979
 
2.9%
0.2 3124
 
3.1%
0.3 8846
8.7%
0.4 7184
7.1%
0.5 894
 
0.9%
0.6 7299
7.2%
0.7 5920
5.8%
0.8 1892
 
1.9%
0.9 2017
 
2.0%
ValueCountFrequency (%)
7.7 15
 
< 0.1%
7.6 16
 
< 0.1%
7.4 92
 
0.1%
7.3 52
 
0.1%
7.2 8
 
< 0.1%
7.1 66
 
0.1%
7 86
 
0.1%
6.9 113
 
0.1%
6.8 146
 
0.1%
6.7 437
0.4%

FOG Index
Real number (ℝ)

High correlation  Missing 

Distinct1977
Distinct (%)2.2%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean9.6674118
Minimum0
Maximum142.24
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:50.375426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q16.61
median8.51
Q311.6
95-th percentile18.68
Maximum142.24
Range142.24
Interquartile range (IQR)4.99

Descriptive statistics

Standard deviation5.3495303
Coefficient of variation (CV)0.55335704
Kurtosis18.277331
Mean9.6674118
Median Absolute Deviation (MAD)2.43
Skewness2.6388406
Sum865281.69
Variance28.617475
MonotonicityNot monotonic
2025-02-06T13:41:50.417662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.04 2075
 
2.0%
10 1943
 
1.9%
9.07 1532
 
1.5%
11.6 1457
 
1.4%
8.51 1441
 
1.4%
8.2 1408
 
1.4%
8 1167
 
1.1%
14.53 1009
 
1.0%
13.2 865
 
0.9%
8.13 802
 
0.8%
Other values (1967) 75806
74.5%
(Missing) 12188
 
12.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.4 230
 
0.2%
0.8 421
0.4%
1 15
 
< 0.1%
1.08 1
 
< 0.1%
1.2 614
0.6%
1.32 7
 
< 0.1%
1.4 48
 
< 0.1%
1.48 14
 
< 0.1%
1.52 5
 
< 0.1%
ValueCountFrequency (%)
142.24 1
 
< 0.1%
106.39 1
 
< 0.1%
104.98 1
 
< 0.1%
86.1 1
 
< 0.1%
84.97 1
 
< 0.1%
80.4 4
< 0.1%
74.51 1
 
< 0.1%
72.32 1
 
< 0.1%
66 1
 
< 0.1%
65.48 1
 
< 0.1%

Flesch Reading Ease
Real number (ℝ)

High correlation  Missing 

Distinct2213
Distinct (%)2.5%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean65.935566
Minimum-555.59
Maximum206.84
Zeros0
Zeros (%)0.0%
Negative2352
Negative (%)2.3%
Memory size794.6 KiB
2025-02-06T13:41:50.459062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-555.59
5-th percentile22.07
Q157.61
median71.82
Q381.29
95-th percentile93.81
Maximum206.84
Range762.43
Interquartile range (IQR)23.68

Descriptive statistics

Standard deviation28.977276
Coefficient of variation (CV)0.43947868
Kurtosis36.546657
Mean65.935566
Median Absolute Deviation (MAD)10.99
Skewness-4.1884291
Sum5901562.8
Variance839.6825
MonotonicityNot monotonic
2025-02-06T13:41:50.502415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.77 761
 
0.7%
73.85 732
 
0.7%
79.26 727
 
0.7%
71.82 715
 
0.7%
81.29 703
 
0.7%
80.28 675
 
0.7%
56.93 671
 
0.7%
64.37 666
 
0.7%
78.25 642
 
0.6%
66.4 637
 
0.6%
Other values (2203) 82576
81.2%
(Missing) 12188
 
12.0%
ValueCountFrequency (%)
-555.59 1
 
< 0.1%
-470.99 2
 
< 0.1%
-386.39 4
 
< 0.1%
-301.79 39
 
< 0.1%
-265.85 1
 
< 0.1%
-260.5 1
 
< 0.1%
-219.22 1
 
< 0.1%
-218.2 4
 
< 0.1%
-217.19 98
0.1%
-177.93 1
 
< 0.1%
ValueCountFrequency (%)
206.84 4
 
< 0.1%
121.22 99
0.1%
120.21 146
0.1%
119.19 149
0.1%
118.68 1
 
< 0.1%
118.18 116
0.1%
117.67 4
 
< 0.1%
117.16 102
0.1%
116.86 1
 
< 0.1%
116.65 6
 
< 0.1%

depth
Real number (ℝ)

High correlation  Missing 

Distinct82867
Distinct (%)92.6%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean0.54634223
Minimum8.19 × 10-18
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:50.545929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.19 × 10-18
5-th percentile0.13008598
Q10.43244792
median0.5792621
Q30.69164552
95-th percentile0.82170573
Maximum1
Range1
Interquartile range (IQR)0.25919759

Descriptive statistics

Standard deviation0.20551205
Coefficient of variation (CV)0.37615992
Kurtosis0.23724848
Mean0.54634223
Median Absolute Deviation (MAD)0.12705512
Skewness-0.64161322
Sum48900.361
Variance0.042235201
MonotonicityNot monotonic
2025-02-06T13:41:50.587495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1130
 
1.1%
0.004922023 217
 
0.2%
9.75 × 10-18124
 
0.1%
2.02 × 10-1798
 
0.1%
0.040497352 93
 
0.1%
0.279730397 90
 
0.1%
0.19844654 80
 
0.1%
1.03 × 10-1778
 
0.1%
1.14 × 10-1777
 
0.1%
9.69 × 10-1867
 
0.1%
Other values (82857) 87451
86.0%
(Missing) 12188
 
12.0%
ValueCountFrequency (%)
8.19 × 10-1831
 
< 0.1%
9.69 × 10-1867
0.1%
9.75 × 10-18124
0.1%
9.89 × 10-181
 
< 0.1%
1.03 × 10-1778
0.1%
1.04 × 10-1738
 
< 0.1%
1.14 × 10-1777
0.1%
1.15 × 10-1760
0.1%
1.3 × 10-1723
 
< 0.1%
1.42 × 10-171
 
< 0.1%
ValueCountFrequency (%)
1 1130
1.1%
0.971529661 1
 
< 0.1%
0.961864631 1
 
< 0.1%
0.961017149 1
 
< 0.1%
0.960951196 1
 
< 0.1%
0.960657503 1
 
< 0.1%
0.960475983 1
 
< 0.1%
0.958671232 1
 
< 0.1%
0.956416259 1
 
< 0.1%
0.956390049 1
 
< 0.1%

breadth
Real number (ℝ)

High correlation  Missing 

Distinct82970
Distinct (%)92.7%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean0.58197322
Minimum0.032643992
Maximum1.6701841
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:50.626785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.032643992
5-th percentile0.2625099
Q10.41224664
median0.54627955
Q30.71761788
95-th percentile1.0427207
Maximum1.6701841
Range1.6375401
Interquartile range (IQR)0.30537124

Descriptive statistics

Standard deviation0.24160824
Coefficient of variation (CV)0.41515354
Kurtosis0.92029194
Mean0.58197322
Median Absolute Deviation (MAD)0.14945944
Skewness0.75969015
Sum52089.513
Variance0.058374542
MonotonicityNot monotonic
2025-02-06T13:41:50.668842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.032643992 1130
 
1.1%
1.099357789 217
 
0.2%
1.214987768 124
 
0.1%
1.311139496 115
 
0.1%
1.154836459 107
 
0.1%
1.097742411 93
 
0.1%
0.721353553 90
 
0.1%
1.248862421 86
 
0.1%
0.732340852 80
 
0.1%
1.177459644 79
 
0.1%
Other values (82960) 87384
85.9%
(Missing) 12188
 
12.0%
ValueCountFrequency (%)
0.032643992 1130
1.1%
0.088938837 1
 
< 0.1%
0.090237534 1
 
< 0.1%
0.092394646 1
 
< 0.1%
0.100507054 1
 
< 0.1%
0.100940848 1
 
< 0.1%
0.102593961 1
 
< 0.1%
0.102907204 1
 
< 0.1%
0.103684607 1
 
< 0.1%
0.106336827 1
 
< 0.1%
ValueCountFrequency (%)
1.670184096 49
< 0.1%
1.667293504 1
 
< 0.1%
1.664931359 2
 
< 0.1%
1.662843386 1
 
< 0.1%
1.659240055 1
 
< 0.1%
1.656232367 1
 
< 0.1%
1.6539588 1
 
< 0.1%
1.647175179 1
 
< 0.1%
1.645908958 1
 
< 0.1%
1.630870649 1
 
< 0.1%

valence
Real number (ℝ)

High correlation  Missing 

Distinct3886
Distinct (%)4.3%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean3.6065883
Minimum1.029
Maximum4.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-06T13:41:50.710058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.029
5-th percentile1.83
Q12.976
median3.839
Q34.346
95-th percentile4.757
Maximum4.99
Range3.961
Interquartile range (IQR)1.37

Descriptive statistics

Standard deviation0.91799465
Coefficient of variation (CV)0.2545327
Kurtosis-0.51175013
Mean3.6065883
Median Absolute Deviation (MAD)0.622
Skewness-0.68241544
Sum322807.69
Variance0.84271417
MonotonicityNot monotonic
2025-02-06T13:41:50.754788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.216 70
 
0.1%
4.199 70
 
0.1%
4.107 64
 
0.1%
4.647 64
 
0.1%
4.084 63
 
0.1%
4.451 63
 
0.1%
4.662 62
 
0.1%
4.154 62
 
0.1%
4.225 62
 
0.1%
4.412 61
 
0.1%
Other values (3876) 88864
87.4%
(Missing) 12188
 
12.0%
ValueCountFrequency (%)
1.029 1
 
< 0.1%
1.032 2
< 0.1%
1.034 1
 
< 0.1%
1.035 1
 
< 0.1%
1.038 1
 
< 0.1%
1.041 1
 
< 0.1%
1.042 1
 
< 0.1%
1.045 2
< 0.1%
1.046 1
 
< 0.1%
1.047 3
< 0.1%
ValueCountFrequency (%)
4.99 1
< 0.1%
4.979 1
< 0.1%
4.976 1
< 0.1%
4.975 1
< 0.1%
4.973 1
< 0.1%
4.968 2
< 0.1%
4.967 2
< 0.1%
4.966 1
< 0.1%
4.964 2
< 0.1%
4.963 2
< 0.1%

sentiment_score_discrete
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing12188
Missing (%)12.0%
Memory size794.6 KiB
4.0
35399 
5.0
23716 
3.0
15397 
2.0
10212 
1.0
4781 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters268515
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row4.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 35399
34.8%
5.0 23716
23.3%
3.0 15397
15.1%
2.0 10212
 
10.0%
1.0 4781
 
4.7%
(Missing) 12188
 
12.0%

Length

2025-02-06T13:41:50.852527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T13:41:50.875128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4.0 35399
39.5%
5.0 23716
26.5%
3.0 15397
17.2%
2.0 10212
 
11.4%
1.0 4781
 
5.3%

Most occurring characters

ValueCountFrequency (%)
. 89505
33.3%
0 89505
33.3%
4 35399
 
13.2%
5 23716
 
8.8%
3 15397
 
5.7%
2 10212
 
3.8%
1 4781
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 268515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 89505
33.3%
0 89505
33.3%
4 35399
 
13.2%
5 23716
 
8.8%
3 15397
 
5.7%
2 10212
 
3.8%
1 4781
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 268515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 89505
33.3%
0 89505
33.3%
4 35399
 
13.2%
5 23716
 
8.8%
3 15397
 
5.7%
2 10212
 
3.8%
1 4781
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 268515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 89505
33.3%
0 89505
33.3%
4 35399
 
13.2%
5 23716
 
8.8%
3 15397
 
5.7%
2 10212
 
3.8%
1 4781
 
1.8%

arousal
Real number (ℝ)

Missing 

Distinct89478
Distinct (%)> 99.9%
Missing12188
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean-0.058501261
Minimum-0.58623818
Maximum0.60756661
Zeros0
Zeros (%)0.0%
Negative38589
Negative (%)37.9%
Memory size794.6 KiB
2025-02-06T13:41:50.911189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.58623818
5-th percentile-0.56770683
Q1-0.20736925
median0.035763963
Q30.11445834
95-th percentile0.23228585
Maximum0.60756661
Range1.1938048
Interquartile range (IQR)0.32182759

Descriptive statistics

Standard deviation0.24979485
Coefficient of variation (CV)-4.2699054
Kurtosis-0.355504
Mean-0.058501261
Median Absolute Deviation (MAD)0.099861283
Skewness-0.76960248
Sum-5236.1554
Variance0.062397467
MonotonicityNot monotonic
2025-02-06T13:41:50.953291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.575615938 2
 
< 0.1%
-0.572121296 2
 
< 0.1%
0.057665373 2
 
< 0.1%
0.092861078 2
 
< 0.1%
0.081766088 2
 
< 0.1%
-0.517506014 2
 
< 0.1%
-0.57743775 2
 
< 0.1%
-0.577283954 2
 
< 0.1%
0.041056407 2
 
< 0.1%
0.13725897 2
 
< 0.1%
Other values (89468) 89485
88.0%
(Missing) 12188
 
12.0%
ValueCountFrequency (%)
-0.586238182 1
< 0.1%
-0.585657485 1
< 0.1%
-0.58438533 1
< 0.1%
-0.583617205 1
< 0.1%
-0.583018747 1
< 0.1%
-0.582906475 1
< 0.1%
-0.582853435 1
< 0.1%
-0.582707796 1
< 0.1%
-0.58268278 1
< 0.1%
-0.582613027 1
< 0.1%
ValueCountFrequency (%)
0.607566615 1
< 0.1%
0.606891388 1
< 0.1%
0.603571831 1
< 0.1%
0.602184191 1
< 0.1%
0.601556126 1
< 0.1%
0.591191042 1
< 0.1%
0.590430496 1
< 0.1%
0.590215761 1
< 0.1%
0.582826869 1
< 0.1%
0.577254739 1
< 0.1%

Interactions

2025-02-06T13:41:46.379794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:41:30.601502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-06T13:41:43.878468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:41:44.675643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:41:45.469893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T13:41:46.341053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-06T13:41:50.997159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Average_RatingDeviation of star ratingsFOG IndexFlesch Reading EaseHelpfulnessHotel_NameNum_of_RatingsRatingarousalbreadthcleanliness_scorecomfort_scoredepthemployee_friendliness_scorefacility_scorehotel_gradeis_photolocation_scoresentiment_score_discretetext_lengthtime_lapsedtitle_lengthvalencevalue_for_money_score
Average_Rating1.000-0.0100.002-0.039-0.0201.000-0.3230.2810.0860.0090.9190.8830.0100.8440.9410.5040.0670.1560.088-0.0350.0530.0070.1620.709
Deviation of star ratings-0.0101.000-0.0070.010-0.0430.184-0.097-0.0380.045-0.0010.0050.001-0.0530.0190.0190.1240.035-0.1040.2590.044-0.045-0.092-0.1140.004
FOG Index0.002-0.0071.000-0.749-0.0030.0140.0300.010-0.0350.053-0.0030.003-0.0530.0010.0000.0090.0220.0340.030-0.103-0.0010.0240.028-0.023
Flesch Reading Ease-0.0390.010-0.7491.0000.0270.023-0.010-0.1050.031-0.126-0.028-0.0340.095-0.040-0.0340.0130.027-0.0260.0700.3010.0040.011-0.121-0.002
Helpfulness-0.020-0.043-0.0030.0271.0000.0320.001-0.0810.006-0.077-0.012-0.0090.054-0.036-0.0130.0120.019-0.0180.0370.1190.0090.030-0.068-0.006
Hotel_Name1.0000.1840.0140.0230.0321.0001.0000.1530.0680.0341.0001.0000.0461.0001.0001.0000.1341.0000.1010.0220.1630.0350.0731.000
Num_of_Ratings-0.323-0.0970.030-0.0100.0011.0001.000-0.0800.0150.004-0.366-0.2480.009-0.307-0.3350.6060.1110.3750.0320.003-0.0780.016-0.047-0.374
Rating0.281-0.0380.010-0.105-0.0810.153-0.0801.0000.2010.0810.2650.264-0.0060.2500.2730.1290.0650.0660.343-0.1880.018-0.0060.5760.197
arousal0.0860.045-0.0350.0310.0060.0680.0150.2011.000-0.0900.0920.1110.1990.0820.0900.0440.0480.0510.2100.1200.0090.0420.3700.041
breadth0.009-0.0010.053-0.126-0.0770.0340.0040.081-0.0901.0000.0060.004-0.7110.0060.0080.0070.0670.0010.099-0.621-0.009-0.1530.080-0.003
cleanliness_score0.9190.005-0.003-0.028-0.0121.000-0.3660.2650.0920.0061.0000.9610.0160.8370.9520.5360.0720.0930.084-0.0230.0840.0060.1550.740
comfort_score0.8830.0010.003-0.034-0.0091.000-0.2480.2640.1110.0040.9611.0000.0240.8100.9430.4830.0610.1120.079-0.0200.0960.0100.1490.646
depth0.010-0.053-0.0530.0950.0540.0460.009-0.0060.199-0.7110.0160.0241.0000.0180.0100.0150.0550.0130.1180.4930.0110.1390.0370.016
employee_friendliness_score0.8440.0190.001-0.040-0.0361.000-0.3070.2500.0820.0060.8370.8100.0181.0000.8160.4250.0900.1090.083-0.0330.0560.0170.1540.683
facility_score0.9410.0190.000-0.034-0.0131.000-0.3350.2730.0900.0080.9520.9430.0100.8161.0000.6530.0690.0640.082-0.0300.076-0.0030.1480.693
hotel_grade0.5040.1240.0090.0130.0121.0000.6060.1290.0440.0070.5360.4830.0150.4250.6531.0000.0610.4260.0320.0150.0730.0100.0380.336
is_photo0.0670.0350.0220.0270.0190.1340.1110.0650.0480.0670.0720.0610.0550.0900.0690.0611.0000.0600.0150.0980.0110.0590.0250.062
location_score0.156-0.1040.034-0.026-0.0181.0000.3750.0660.0510.0010.0930.1120.0130.1090.0640.4260.0601.0000.0550.005-0.0220.0580.0730.019
sentiment_score_discrete0.0880.2590.0300.0700.0370.1010.0320.3430.2100.0990.0840.0790.1180.0830.0820.0320.0150.0551.0000.0930.0220.0490.8000.070
text_length-0.0350.044-0.1030.3010.1190.0220.003-0.1880.120-0.621-0.023-0.0200.493-0.033-0.0300.0150.0980.0050.0931.0000.0030.224-0.182-0.015
time_lapsed0.053-0.045-0.0010.0040.0090.163-0.0780.0180.009-0.0090.0840.0960.0110.0560.0760.0730.011-0.0220.0220.0031.0000.0090.0160.081
title_length0.007-0.0920.0240.0110.0300.0350.016-0.0060.042-0.1530.0060.0100.1390.017-0.0030.0100.0590.0580.0490.2240.0091.0000.0050.005
valence0.162-0.1140.028-0.121-0.0680.073-0.0470.5760.3700.0800.1550.1490.0370.1540.1480.0380.0250.0730.800-0.1820.0160.0051.0000.127
value_for_money_score0.7090.004-0.023-0.002-0.0061.000-0.3740.1970.041-0.0030.7400.6460.0160.6830.6930.3360.0620.0190.070-0.0150.0810.0050.1271.000

Missing values

2025-02-06T13:41:47.268828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-06T13:41:47.412750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-06T13:41:47.905349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Hotel_NameReview_TextRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_lengthtext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadthvalencesentiment_score_discretearousal
0studios2letPerfect location with good connections and shops and pubs10.07.611670.00.00.0Exceptional3.08.37.57.97.87.69.32024.12.51.09.02182.412.4962.340.4165570.8433824.7215.00.104254
1studios2letThe room had everything you needed. Near to amenities, was good room for price just needs little updatingThe bed was so hard it felt like sleeping on a hard floor, you had to make sure you had something on your feet as flooring pinched you feet needs changing8.07.611670.00.00.0Very good3.08.37.57.97.87.69.32024.12.52.048.030.410.4380.960.5786330.4319152.9573.0-0.318073
2studios2letConveniently nearby St. Pancras, very small but clean and pleasant room (first floor with small balcony to street side). Interesting area.Luggage service can be improved by offering to lock luggage up instead of it just being put into the hall with all risks on the guests.8.07.611670.00.00.0Convenient location3.08.37.57.97.87.69.32024.12.52.046.040.47.8672.870.5772590.4870194.0444.0-0.034590
3studios2letReception staffed 24 hours a day.All good.9.07.611670.00.00.0Peaceful position in an elegant street close to 3 major stations and the Bloomsbury area.3.08.37.57.97.87.69.32024.12.515.07.041.48.5181.290.3546810.7017854.5515.00.050737
4studios2letVery convenient to Kings Cross and the cityA little dated could do with a lick of paint8.07.611670.00.00.0Great little gem in the city centre3.08.37.57.97.87.69.32024.12.57.017.050.49.1588.060.6818630.8383734.2004.0-0.276062
5studios2letLocated in a quiet area but close to Kings Cross station so getting around was easy. Several little pubs nearby for dining and some good coffee shops too.There is no lift so dragging a heavy suitcase up and down stairs was challenging. We had booked a room with terrace but the outdoor space was really minuscule - not what we had expected from the photos.7.07.611670.00.00.0Convenient, quiet location.3.08.37.57.97.87.69.32024.12.53.065.050.68.9072.160.5797460.4303843.5284.0-0.233775
6studios2letIt's spacious, good value and so very quiet for London.You sometimes have to wriggle the loo flusher to stop it running and running9.07.611670.00.00.0Superb3.08.37.57.97.87.69.32024.12.51.023.051.44.6076.720.4720330.4674034.2134.0-0.030927
7studios2letLocationLot of stairs (bad knee)9.07.611670.00.00.0Ideal location for travelling round3.08.37.57.97.87.69.32024.12.55.05.061.410.0066.400.4511540.3797002.3712.0-0.560517
8studios2letLocation was great, so near the stationWe were on the top floor, six flights of stairs and no lift.\nHeating was on 247 full temperature and no means of reducing it!7.07.611670.00.00.0Perfect location,3.08.37.57.97.87.69.32024.12.52.031.060.611.3681.120.5074670.4995332.1162.00.083809
9studios2letThe location which is excellent for public transport and local dining. \nFriendly staffed reception where we could leave our travel bags all day after checking out.The climb up 3 flights of stairs was exhausting but it was our choice.\nIt was a small room and the kitchen facilities were very sparse ( but we didn't need them)8.07.611670.00.00.0Ideal accommodation for a short stay in London near St Pancreas station3.08.37.57.97.87.69.32024.12.512.057.070.49.1774.190.7762670.4300354.2564.0-0.058265
Hotel_NameReview_TextRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_lengthtext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadthvalencesentiment_score_discretearousal
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Duplicate rows

Most frequently occurring

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